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Langchain v0.3 Agents P4: LangGraph Build & Visualize Graph Run a Chatbot Understand Agent Workflow

Education

Introduction

Welcome to our series where we explore the creation of custom AI agents using the latest large language models such as OpenAI's GPT-4, as well as various tools from the Langchain ecosystem. In this article, we’ll focus on the LangGraph workflow, ultimately paving the way for the development of multi-agent orchestration frameworks. As we dive into this sophisticated topic, we'll take baby steps to ensure clarity and comprehension.

Understanding LangGraph Architecture

Before getting into the hands-on coding, let’s walk through the architecture of a LangGraph workflow. In this framework, each agent or function is referred to as a "node," and connections between these nodes are established through "edges." There are two types of edges:

  • Normal Edges: These connect two nodes simply, without any condition.
  • Conditional Edges: These can connect multiple nodes based on certain conditions.

For instance, in our chatbot example, we’ll use specific functions (nodes) such as a chatbot, tools for searching, and a SQL query node that interacts with a database.

Let’s visualize this in the context of a customer service scenario:

  1. User Interaction: A user engages with the chatbot.
  2. Greeting and Request Handling: The chatbot greets the user and processes their request.
  3. Decision Point: Based on the request, a conditional edge determines which agent to trigger:
    • General Info Node: For frequently asked questions.
    • Customer Issue Resolution Node: For issue categorization (positive feedback, negative feedback, human escalation).
    • Order Status Info Node: For inquiries about order statuses.

If the drawn-out scenario leads to an escalation, a human agent can be signaled. Through a SQL tool node, the bot will check the order status when required, completing the user’s journey.

A state machine will maintain conversation context, keeping track of interactions, although initial messages aren’t persisted. In upcoming segments, we will address memory storage in a database.

Building Our First LangGraph Chatbot

Now, let’s jump into the coding aspect. Here’s the workflow we will follow:

  1. Install Necessary Libraries
  2. Set Up Environment Variables
  3. Load Libraries
  4. Build and Visualize the Graph
  5. Create and Run a Chatbot

Step 1: Installation

We will be using libraries such as Langchain, OpenAI, SQLite for memory (to be explored in the next part), LangGraph, and some others. Our architecture relies on the latest Langchain version (0.3.5).

Step 2: Environment Preparation

We will set up an OpenAI key variable to access the GPT-4 model.

Step 3: Load Libraries and Build Graph

We create a structure for our messaging and invoke the state graph. Then, we add nodes to capture the chatbot functionality. It’s vital to note that this graph will also determine how the chatbot operates.

Step 4: Visualization

Once we compile our graph, we visualize it in multiple formats using libraries like IPython. We could see a basic flow represented in an image, as well as a text-based visualization.

Step 5: Running the Chatbot

We implement a basic infinite loop that processes user input and provides responses. We’ve set triggers for exiting the chat and special messages to ensure smooth operation.

Upon testing the chatbot, it’s evident that while it answers well, its lack of real-time information leads to limitations, like outdated responses regarding current events. This is where we will enhance our bot in the next video by integrating tool nodes for searching and adding SQL-based memory for persistent context.

In conclusion, we have stepped through the fundamental components of creating a basic chatbot using LangGraph, setting the stage for improvements and functionality in future episodes.

Keywords

LangGraph, Chatbot, Nodes, Edges, State Machine, Conditional Edge, SQL, Memory, User Interaction, Langchain, OpenAI GPT-4.

FAQ

1. What is LangGraph?
LangGraph is a framework within the Langchain suite that allows for building workflows using agents represented as nodes connected by edges.

2. What are nodes and edges in LangGraph?
Nodes represent agents or functions in a workflow, while edges represent the connections between the nodes, which can be either normal or conditional.

3. How does a chatbot built with LangGraph work?
A chatbot processes user input, follows conditional edges to determine which node to engage, and ultimately replies to the user based on the collected information.

4. What will be added in the next video?
In the next installment, we will enhance the chatbot by integrating tool nodes for real-time information retrieval and adding SQL-based memory for persistent chat history.